基于覆盖约简的个性化协同过滤推荐方法

doi:10.16451/j.cnki.issn1003-6059.201907004

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Abstract

Collaborative filtering(CF) cannot provide personalized recommendation with both good accuracy and diversity. To address this problem, a covering reduction collaborative filtering(CRCF) is proposed in this paper. The covering reduction algorithm in covering based rough sets is combined with user reduction in CF, and redundant elements of covering are matched with redundant users of a neighbor. The redundant users are removed by covering reduction algorithm to ensure high effectiveness of the neighbor of a target user in CF. Experimental results on public datasets indicate that CRCF provides personalized recommendations for target users with both satisfactory accuracy and diversity in sparse data environment.

Key words： Recommender System Collaborative Filtering Covering Reduction Personalized Recommendation

Received: 20 March 2019

ZTFLH:

TP 393

Fund:

Support by National Natural Science Foundation of China(No.61772252), Ph.D. Start-up Foundation of Liaoning Normal University(No.BS2018L008)

About author:: ZHANG Zhipeng, Ph.D., lecturer. His research interests include data mining and recommender system.ZHANG Yao, Ph.D., lecturer. Her research interests include pattern recognition and simulation analysis.REN Yonggong(Corresponding author), Ph.D., professor. His research interests include artificial intelligence and data mining.

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	ZHANG Zhipeng
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Cite this article:

ZHANG Zhipeng,ZHANG Yao,REN Yonggong. Personalized Collaborative Filtering Recommendation Approach Based on Covering Reduction[J]. , 2019, 32(7): 607-614.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/607

[1] ADOMAVICIUS G, TUZHILIN A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[2] BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender System Survey. Knowledge-Based Systems, 2013, 46: 109-132.
[3] HAMEED M A, JADAAN O A, RAMACHANDRAM S. Collaborative Filtering Based Recommendation System: A Survey. International Journal on Computer Science and Engineering, 2012, 4(5): 859-876.
[4] SYMEONIDIS P, NANOPOULOS A, PAPADOPOULOS A N, et al. Collaborative Recommender Systems: Combining Effectiveness and Efficiency. Expert Systems with Applications, 2008, 34(4): 2995-3013.
[5] GUO G B, ZHANG J, THALMANN D. Merging Trust in Collaborative Filtering to Alleviate Data Sparsity and Cold Start. Knowledge-Based Systems, 2014, 57: 57-68.
[6] SHI Y, KARATZOGLOU A, BALTRUNAS L, et al. TFMAP: Optimizing MAP for Top-n Context-Aware Recommendation // Proc of the 35th ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2012: 155-164.
[7] 李琳,刘锦行,孟祥福,等.融合评分矩阵与评论文本的商品推荐模型.计算机学报, 2018, 41(7): 1559-1573.
(LI L, LIU J X, MENG X F, et al. Recommendation Models by Exploiting Rating Matrix and Review Text. Chinese Journal of Computers, 2018, 41(7): 1559-1573.)
[8] 刘华锋,景丽萍,于剑.融合社交信息的矩阵分解推荐方法研究综述.软件学报, 2018, 29(2): 340-362.
(LIU H F, JING L P, YU L. Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information. Journal of Software, 2018, 29(2): 340-362.)
[9] KIM H N, JI A T, HA I, et al. Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation. Electronic Commerce Research and Applications, 2010, 9(1): 73-83.
[10] LIKA B, KOLOMVATSOS K, HADJIEFTHYMIADES S. Facing the Cold Start Problem in Recommender Systems. Expert Systems with Applications, 2014, 41(4): 2065-2073.
[11] NIU J W, WANG L, LIU X T, et al. FUIR: Fusing User and Item Information to Deal with Data Sparsity by Using Side Information in Recommendation Systems. Journal of Network and Computer Applications, 2016, 70: 41-50.[12] BELLOGIN A, FERNANDEZ-TOBIAS I, CANTADOR I, et al. Neighbor Selection for Cold Users in Collaborative Filtering with Positive-Only Feedback // Proc of the Conference of the Spanish Association for Artificial Intelligence. Berlin, Germany: Springer, 2018: 3-12.
[13] PAWLAK Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[14] ZAKOWSKI W. Approximations in the Space (u,π). Demonstratio Mathematica, 1983, 16(3): 761-769.
[15] ZHU W, WANG F Y. Reduction and Axiomization of Covering Generalized Rough Sets. Information Sciences, 2003, 152: 217-230.
[16] ZHU W, WANG F Y. On Three Types of Covering Based Rough Sets. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(8): 1131-1144.
[17] HERLOCKER J L, KONSTAN J A, BORCHERS A, et al. An Algorithmic Framework for Performing Collaborative Filtering // Proc of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1999: 230-237.
[18] HERLOCKER J, KONSTAN J A, RIEDL J. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval, 2002, 5(4): 287-310.